def main(c_success=C_SUCCESS):
#mf = MWKFile('../analysis/data_merged/Chabo_20110426_MovieGallant110413_S110204_RefAA_001.mwk')
mf = MWKFile('../analysis/data_merged/Chabo_20110331_RSVPNicole305_S110204_RefAA_001.mwk')
mf.open()
#br = BRReader('../analysis/data_nev/Chabo_20110426_MovieGallant110413_S110204_RefAA_001.nev')
br = BRReader('../analysis/data_nev/Chabo_20110331_RSVPNicole305_S110204_RefAA_001.nev')
br.open()
if PLOT_ONLY_ABV:
adj_reject = 5./3.
new_thr = {}
for ch in br.chn_info:
lthr = br.chn_info[ch]['low_thr']; hthr = br.chn_info[ch]['high_thr']
if lthr == 0: thr0 = hthr
else: thr0 = lthr
new_thr[ch] = thr0 * adj_reject * 0.249
print '*', ch, new_thr[ch]
toc = xget_events(mf, codes=['#merged_data_toc'])[0].value
# MAC-NSP time translation
if OVERRIDE_DELAY_US != None:
t_delay = toc['align_info']['delay']
t_adjust = int(np.round(OVERRIDE_DELAY_US - t_delay))
else:
t_adjust = 0
# when the visual stimuli presented is valid?
t_success = [ev.time for ev in mf.get_events(codes=[c_success])]
t_success = np.array(t_success)
img_onset, img_id = get_stim_info(mf)
print 'Len =', len(img_onset)
i_plot_ac = 0
i_spk = 0
i_spk2 = 0
i_spk_nvis = 0
i_spk_vis = 0
M = np.zeros((MAX_SPK, DIM))
M2 = np.zeros((MAX_SPK, DIM))
Mnvis = np.zeros((MAX_SPK, DIM))
Mvis = np.zeros((MAX_SPK, DIM))
t = (np.arange(DIM) - 11) * 1000. / 30000. # in ms
#t = np.arange(len(DIM)) # in index
for t0 in img_onset:
if i_spk >= MAX_SPK: break
if i_spk_nvis >= MAX_SPK: break
if i_spk_vis >= MAX_SPK: break
if np.sum((t_success > t0) & (t_success < (t0 + T_SUCCESS))) < 1: continue
spks = mf.get_events(codes=['merged_spikes'], time_range=[t0 +START, t0 + END])
for spk in spks:
ch = spk.value['id']
ts = spk.time
if ch != CH: continue
offset = spk.value['foffset']
wav = br.read_once(pos=offset, proc_wav=True)['waveform']
y = np.array(wav) * 0.249 # in uV
if PLOT_ONLY_DOWN:
if y[12] > y[11]: continue
if PLOT_ONLY_ABV:
wav = set_new_threshold(wav, new_thr[ch], rng=(11, 13), i_chg=32)
if wav == None: continue
if PLOT_ONLY_EXCHG:
if np.max(y[:32]) < 0: continue
t_rel = ts + t_adjust - t0
# print t_rel
# -- monitor noise?
if (t_rel/1000.) % NPERIOD < 1. or (t_rel/1000.) % NPERIOD > (NPERIOD - 1):
if t_rel > -50000 and t_rel < 50000:
M[i_spk] = y
i_spk += 1
pl.figure(ch)
pl.plot(t, y, 'k-')
pl.title('Noise responses (OFF region)')
pl.xlabel('Time/ms')
pl.ylabel(r'Response/$\mu$V')
elif t_rel > 70000 and t_rel < 170000:
M2[i_spk2] = y
i_spk2 += 1
pl.figure(1000 + ch)
pl.plot(t, y, 'k-')
pl.title('Noise responses (ON region)')
pl.xlabel('Time/ms')
pl.ylabel(r'Response/$\mu$V')
elif (t_rel/1000.) % NPERIOD > 2. and (t_rel/1000.) % NPERIOD < (NPERIOD - 2):
if t_rel > -50000 and t_rel < 50000:
Mnvis[i_spk_nvis] = y
i_spk_nvis += 1
pl.figure(2000 + ch)
pl.plot(t, y, 'k-')
pl.title('Non-noise blank responses')
pl.xlabel('Time/ms')
pl.ylabel(r'Response/$\mu$V')
elif t_rel > 70000 and t_rel < 170000:
Mvis[i_spk_vis] = y
i_spk_vis += 1
pl.figure(3000 + ch)
pl.plot(t, y, 'k-')
pl.title('Non-noise visual responses')
pl.xlabel('Time/ms')
pl.ylabel(r'Response/$\mu$V')
i_plot_ac += 1
M = M[:i_spk]
# pl.figure()
# pl.hist(np.ravel(M[:,12] - M[:,11]), bins=20)
print 'i_spk =', i_spk
print 'i_spk2 =', i_spk2
print 'i_spk_nvis =', i_spk_nvis
print 'i_spk_vis =', i_spk_vis
print 'i_plot_ac =', i_plot_ac
M = M[:i_spk,:]
M2 = M2[:i_spk2,:]
Mnvis = Mnvis[:i_spk_nvis,:]
Mvis = Mvis[:i_spk_vis,:]
pl.figure()
xb, yb = myhist(np.min(M,axis=1), norm='peak')
xb2, yb2 = myhist(np.min(M2,axis=1), norm='peak')
xg, yg = myhist(np.min(Mnvis,axis=1), norm='peak')
xv, yv = myhist(np.min(Mvis,axis=1), norm='peak')
pl.plot(xb, yb, 'r-', label='Noise responses (OFF)')
pl.plot(xb2, yb2, 'm-', label='Noise responses (ON)')
pl.plot(xg, yg, 'g-', label='Non-noise responses (OFF)')
pl.plot(xv, yv, 'b-', label='Non-noise responses (ON)')
#pl.axvline(ptbad.mean(), color='r',alpha=0.3)
#pl.axvline(ptgood.mean(), color='b',alpha=0.3)
#pl.axvline(ptvis.mean(), color='g',alpha=0.3)
pl.xlabel(r'Peak response/$\mu$V')
pl.ylabel('Normalized probability')
pl.legend(loc='upper left')
pl.figure()
# --
m = M.mean(axis=0); s = M.std(axis=0, ddof=1)
pl.plot(t, m, 'r-', label='Noise responses (OFF)')
pl.fill_between(t, m-s, m+s, color='r', alpha=0.2)
# --
m = Mnvis.mean(axis=0); s = Mnvis.std(axis=0, ddof=1)
pl.plot(t, m, 'g-', label='Non-noise responses (OFF)')
pl.fill_between(t, m-s, m+s, color='g', alpha=0.2)
# --
pl.xlabel('Time/ms')
pl.ylabel(r'Response/$\mu$V')
pl.legend(loc='lower right')
pl.figure()
# --
m = M2.mean(axis=0); s = M2.std(axis=0, ddof=1)
pl.plot(t, m, 'm-', label='Noise responses (ON)')
pl.fill_between(t, m-s, m+s, color='m', alpha=0.2)
# --
m = Mvis.mean(axis=0); s = Mvis.std(axis=0, ddof=1)
pl.plot(t, m, 'b-', label='Non-noise responses (ON)')
pl.fill_between(t, m-s, m+s, color='b', alpha=0.2)
# --
pl.xlabel('Time/ms')
pl.ylabel(r'Response/$\mu$V')
pl.legend(loc='lower right')
pl.show()
def set_new_threshold_rng(wav, thr):
return set_new_threshold(wav, thr, rng=(11, 13), i_chg=32)